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Question: In Exercise 7, find a unit vector x at which Ax has maximum length.

Short Answer

Expert verified

The unit vector is, \(\left( {\begin{array}{*{20}{c}}{\frac{2}{{\sqrt 5 }}}\\{\frac{1}{{\sqrt 5 }}}\end{array}} \right)\).

Step by step solution

01

Write the result from Exercise 7

For the matrix \(A = \left( {\begin{array}{*{20}{c}}2&{ - 1}\\2&2\end{array}} \right)\), the eigenvalues of matrix \({A^T}A\) are 9 and 4.

02

Find the vector x for which length of Ax is maximum

As the maximum eigenvalue is 9, so the maximum value of \(\left\| {A{\bf{x}}} \right\|\) is 9.

The eigenvectors of the matrix for \(\lambda = 9\) are:

\({A^T}A - 9I = \left( {\begin{array}{*{20}{c}}{ - 1}&2\\2&{ - 4}\end{array}} \right)\)

Write the row-reduced Augmented matrix as:

\(\begin{array}{c}M = \left( {\begin{array}{*{20}{c}}{ - 1}&2&0\\2&{ - 4}&0\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}{ - 1}&2&0\\0&0&0\end{array}} \right)\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\left( {{R_2} \to {R_2} + 2{R_1}} \right)\end{array}\)

So, the corresponding eigenvector is \(\left( {\begin{array}{*{20}{c}}2\\1\end{array}} \right)\).

Normalize the eigenvector \(\left( {\begin{array}{*{20}{c}}2\\1\end{array}} \right)\) is:

\(\begin{array}{c}{{\bf{v}}_1} = \frac{1}{{\sqrt {{2^2} + {1^2}} }}\left( {\begin{array}{*{20}{c}}2\\1\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}{\frac{2}{{\sqrt 5 }}}\\{\frac{1}{{\sqrt 5 }}}\end{array}} \right)\end{array}\)

Thus, the vector for which length of Ax is maximum is \(\left( {\begin{array}{*{20}{c}}{\frac{2}{{\sqrt 5 }}}\\{\frac{1}{{\sqrt 5 }}}\end{array}} \right)\).

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Most popular questions from this chapter

Classify the quadratic forms in Exercises 9-18. Then make a change of variable, \({\bf{x}} = P{\bf{y}}\), that transforms the quadratic form into one with no cross-product term. Write the new quadratic form. Construct P using the methods of Section 7.1.

10. \({\bf{2}}x_{\bf{1}}^{\bf{2}} + {\bf{6}}{x_{\bf{1}}}{x_{\bf{2}}} - {\bf{6}}x_{\bf{2}}^{\bf{2}}\)

Construct a spectral decomposition of A from Example 3.

Determine which of the matrices in Exercises 7–12 are orthogonal. If orthogonal, find the inverse.

8. \(\left( {\begin{aligned}{{}}1&{\,\,\,1}\\1&{ - 1}\end{aligned}} \right)\)

In Exercises 25 and 26, mark each statement True or False. Justify each answer.

26.

  1. There are symmetric matrices that are not orthogonally diagonizable.
  2. b. If \(B = PD{P^T}\), where \({P^T} = {P^{ - {\bf{1}}}}\) and D is a diagonal matrix, then B is a symmetric matrix.
  3. c. An orthogonal matrix is orthogonally diagonizable.
  4. d. The dimension of an eigenspace of a symmetric matrix is sometimes less than the multiplicity of the corresponding eigenvalue.

Question: Find an SVD of each matrix in Exercise 5-12. (Hint: In Exercise 11, one choice for U is \(\left( {\begin{array}{*{20}{c}}{ - \frac{{\bf{1}}}{{\bf{3}}}}&{\frac{{\bf{2}}}{{\bf{3}}}}&{\frac{{\bf{2}}}{{\bf{3}}}}\\{\frac{{\bf{2}}}{{\bf{3}}}}&{ - \frac{{\bf{1}}}{{\bf{3}}}}&{\frac{{\bf{2}}}{{\bf{3}}}}\\{\frac{{\bf{2}}}{{\bf{3}}}}&{\frac{{\bf{2}}}{{\bf{3}}}}&{ - \frac{{\bf{1}}}{{\bf{3}}}}\end{array}} \right)\). In Exercise 12, one column of U can be \(\left( {\begin{array}{*{20}{c}}{\frac{{\bf{1}}}{{\sqrt {\bf{6}} }}}\\{ - \frac{{\bf{2}}}{{\sqrt {\bf{6}} }}}\\{\frac{{\bf{1}}}{{\sqrt {\bf{6}} }}}\end{array}} \right)\).)

11. \(\left( {\begin{array}{*{20}{c}}{ - {\bf{3}}}&{\bf{1}}\\{\bf{6}}&{ - {\bf{2}}}\\{\bf{6}}&{ - {\bf{2}}}\end{array}} \right)\)

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